Abstract
Background
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality globally, necessitating finding novel therapeutic targets. Mitochondrial autophagy (mitophagy) and ferroptosis have emerged as promising avenues in cancer research. This study aimed to identify mitophagy- and ferroptosis-related genes (MiFeRGs) in LUAD and develop a prognostic risk model based on these genes.
Methods
Integration of transcriptomic data from the TCGA dataset with MiFeRG databases was performed. Subsequently, differentially expressed MiFeRGs were identified. A prognostic risk model was developed using univariate, LASSO, and multivariate Cox regression analyses. Survival analysis, immune infiltration assessment, and GSEA analysis were conducted to evaluate the prognostic value and potential mechanisms of MiFeRGs in LUAD. Expression levels and functions of prognostic MiFeRGs were further validated in cells.
Results
A total of 136 differentially expressed MiFeRGs were identified, with enrichment in signaling pathways associated with cancer progression. Seven MiFeRGs (ARPC1A, AURKA, BRD2, HNRNPL, METTL3, NR4A1, and TRPM2) were selected for the prognostic risk model. The model showed wide applicability across various clinical parameters. Furthermore, the PPI network revealed potential associations between MiFeRGs and TCR-related genes, with hub MiFeRG AURKA and TCR-related gene AKT1 having the highest degree values. The levels of AURKA were upregulated in the LUAD cell line and tumor tissues. Moreover, AURKA was associated with mTORC1 activation.
Conclusion
The identified MiFeRGs and the developed prognostic risk model provide valuable insights into the molecular mechanisms underlying LUAD progression and offer potential prognostic and therapeutic implications for clinical management.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-025-02216-2.
Keywords: Lung adenocarcinomas, Mitochondrion, Autophagy, Ferroptosis, Prognosis, Biomarker
Introduction
Lung cancer is a highly prevalent and deadly cancer worldwide, accounting for 22% of all cancer-related deaths [1]. The majority of lung cancers are non-small cell lung cancer (NSCLC), comprising over 80% [2]. Besides, lung adenocarcinoma (LUAD) is the most common histological subtype of NSCLC, accounting for about 40% of lung cancer [3]. Although the detection and treatment of lung cancer have advanced, most lung cancer patients are typically diagnosed at advanced stages [4]. The prognosis for lung cancer patients including LUAD is poor, with a less than 20% 5-year survival rate, but if diagnosed early, the survival rate increases to 60% [5–7]. Therefore, it is urgent to identify effective treatment targets for LUAD.
Ferroptosis is a form of iron-dependent cell death. Increasing research suggests it as a potential approach for cancer treatment [8, 9]. The main morphological features of ferroptosis include mitochondrial membrane shrinkage, increased membrane density, and reduced mitochondrial cristae [10]. Mitochondrion, being the primary source of intracellular reactive oxygen species (ROS), is closely associated with ferroptosis. The mitochondrion is a vital organelle that regulates cellular energy production, redox homeostasis, and apoptosis [11]. Cancer cells often exhibit high levels of mitochondrial metabolism to sustain their elevated energy demands. Impaired mitochondrial function leads to increased ROS production and cellular oxidative damage [12]. Mitochondrial autophagy (mitophagy) selectively removes damaged mitochondria and plays a critical role in maintaining mitochondrial homeostasis [13, 14]. Mitophagy is initiated to preserve cell survival, but under prolonged cellular damage, it may also lead to cell death [15]. Aberrant levels of mitophagy, either too high or too low, affect normal cellular function and are highly implicated in cancer development [16]. During cancer progression, mitophagy can promote both glycolysis and oxidative phosphorylation to supply enough ATP for cancer cell survival, invasion, and metastasis. Conversely, excessive mitophagy can also induce cancer cell death [17]. With advancing research, interactions between mitophagy and ferroptosis have been identified. Mitophagy can either enhance or inhibit ferroptosis. Some drugs that regulate mitophagy also influence ferroptosis [18]. Maintaining mitochondrial homeostasis plays a crucial role in preventing ferroptosis.
In this study, we developed a prognostic model based on mitophagy- and ferroptosis-related genes (MiFeRGs) using public datasets. By analyzing gene expression data from large patient cohorts in the TCGA database, we sought to identify gene signatures associated with prognosis and evaluate their predictive value in stratifying patients into high- and low-risk groups. This study aimed to identify novel targets for improving prognostic accuracy and facilitating personalized treatment approaches for LUAD patients.
Methods
Data download and preprocessing
RNA sequencing data and clinical data of patients with LUAD were downloaded from the TCGA database (https://portal.gdc.cancer.gov). The clinical data of the TCGA-LUAD cohort are shown in Table S1. Principal component analysis (PCA) was performed to show the distribution of TCGA-LUAD samples using the stats and prcomp packages in R, and results were visualized using the ggplot package. Mitophagy-related genes were acquired from the GeneCard database (https://www.genecards.org), and ferroptosis-related genes were screened in the FerrDb database (http://www.zhounan.org/ferrdb/current). The mitophagy-related and ferroptosis-related genes were shown in Table S2.
Identification of differentially expressed MiFeRGs
Firstly, using the Limma package in R, differentially expressed genes (DEGs) in the normal and tumor samples were identified, with p < 0.05 and fold change = 1.5. The DEGs were visualized through a volcano plot using the R package ggplot2. Then, the overlapping genes of DEGs, mitophagy-related genes, and ferroptosis-related genes were screened out and considered as differentially expressed MiFeRGs through a Venn diagram. The Venn diagram was generated on the website https://jvenn.toulouse.inra.fr/app/example.html.
Development and evaluation of prognostic risk model
To identify prognosis-related MiFeRGs from the initially identified MiFeRGs, a stepwise selection strategy was employed. First, univariate Cox regression analysis was performed using SPSS to evaluate the association between each gene’s expression and overall survival (OS) in TCGA-LUAD cohort. Genes with p < 0.05 were retained as potential prognostic factors. Next, to reduce dimensionality and eliminate redundancy, LASSO Cox regression analysis was conducted using the “glmnet” R package. Ten-fold cross-validation was applied to determine the optimal penalty parameter (λ = 0.011), and genes with non-zero coefficients at the optimal λ value were selected. Finally, these genes were subjected to multivariate Cox regression analysis in SPSS to assess their independent prognostic significance. Genes that remained statistically significant in the multivariate analysis were designated as hub prognostic MiFeRGs. The forest plots for Cox regression analysis were visualized through forestploter package. A prognostic model was constructed based on the hub MiFeRGs.
To evaluate the predictive performance, LUAD patients in the TCGA database were categorized into low-risk or high-risk groups based on the optimal truncation value of risk scores. The risk score of each patient was calculated as “Risk score = EXPRNA1*COEFRNA1 + EXPRNA2*COEFRNA2 + … + EXPRNAi*COEFRNAi”, where EXPRNAi is gene’s expression level, and COEFRNAi is the multivariate Cox regression coefficient. Kaplan-Meier (K-M) curves were used to compare the differences in survival between low-risk and high-risk groups, and the receiver operating characteristic (ROC) curve was applied to evaluate the model’s accuracy using survival and survminer packages. The relevance of risk score with clinical features, including age, gender, TNM stage, and pathological stage, was analyzed.
Functional enrichment analysis
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions of MiFeRGs using R packages clusterProfiler and org.Hs.eg.db. Gene set variation analysis (GSVA) analysis was performed through R packages GSEABase and GSVA to compare different KEGG pathways between two different risk groups. Gene set enrichment analysis (GSEA) was conducted to screen hub prognostic MiFeRG-related pathways using the GSVA package. h.all.v2023.2.Hs.symbols served as a reference gene set, and the results were visualized using ggplot2.
TME analysis
Immune cell infiltration was analyzed using the CIBERSORT algorithm of the IBOR package in R. To assess the T cell exhaustion, T cell receptor (TCR)-related genes were searched from the ImmPort (https://www.dev.immport.org/home). Expression levels of TCR-related genes in low-risk and high-risk groups and their correlation with risk scores were further explored.
Protein-protein interaction (PPI) network
The interactions among TCR-related genes and their interactions with the hub prognostic MiFeRGs were visualized through the PPI networks. The STRING database (http://stringdb.org) generated the PPI networks, which were visualized using Cytohubba in Cytoscape 3.7.0.
Cell culture
Human lung normal epithelial cell line BEAS-2B (#CELL-C0496, Daucell Biotechnology Co., Ltd., Wuhan, China) and human LUAD cell lines A549 (#CL-0016, Pricella Biotechnology Co., Ltd., Wuhan, China), H1975 (#CL-0298, Pricella), and NCI-H2009 (#CL-0762, Pricella) were cultured in RPMI medium 1640, supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 µg/mL streptomycin. The cells were incubated at 37°C with 5% CO2.
Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from cells using Trizol reagent (Invitrogen, CA, USA) and quantified with the NanoDrop 8000 spectrophotometer (ThermoFisher, CA, USA). cDNA synthesis was performed using the FastKing gDNA Dispelling RT SuperMix (TIANGEN, Beijing, China). For qRT-PCR analysis, a CFX96 Touch Real-Time PCR System (Bio-Rad, CA, USA) was employed with gene-specific primers, while GAPDH served as the reference gene. The qRT-PCR reaction utilized One-Step SYBR Green Master Mix (ThermoFisher) under cycling conditions of 30 s at 95°C, followed by 40 cycles of 3 s at 95°C and 20 s at 60°C. Relative mRNA expression levels of genes were estimated using the 2−ΔΔCt method. Primer sequences used in the study are as Table 1.
Table 1.
Primer sequences
| Genes | Sequences | |
|---|---|---|
| ARPC1A | Forward | 5’-CGATCCCAGCTTTCTCTCCT-3’ |
| Reverse | 5’-ATTGGGACTGAGGGCAATGA-3’ | |
| AURKA | Forward | 5’-CCAGGGCTGCCATATAACCT-3’ |
| Reverse | 5’-GTTAAGGCACACCTGCTGAG-3’ | |
| BRD2 | Forward | 5’-AAGCCAGGACGAGTTACCAA-3’ |
| Reverse | 5’-TCCGGTAGACCCAGTTTGAC-3’ | |
| HNRNPL | Forward | 5’-AGACTCTGGGCTTCCTGAAC-3’ |
| Reverse | 5’-TGTCTTCCTGCTCAGATGGG-3’ | |
| METTL3 | Forward | 5’-GGCTGGGAGACTAGGATGTC-3’ |
| Reverse | 5’-AGAGTCCAGCTGCTTCTTGT-3’ | |
| NR4A1 | Forward | 5’-GGCAAGCTCATCTTCTGCTC-3’ |
| Reverse | 5’-CAGGGACATCGACAAGCAAG-3’ | |
| TRPM2 | Forward | 5’-CAAGGACAAGCTCTGTCTGC-3’ |
| Reverse | 5’-GGTGGTTACTGGAGCCTTCT-3’ | |
| GAPDH | Forward | 5’-GGAGCGAGATCCCTCCAAAAT-3’ |
| Reverse | 5’-GGCTGTTGTCATACTTCTCATGG-3’ | |
Cell transfection
Small interfering RNAs (siRNAs) targeting AURKA, BRD2, TRPM2, ARPC1A, HNRNPL, and METTL3, as well as the NR4A1, ARPC1A, HNRNPL, and METTL3 overexpression plasmid and corresponding negative controls, were synthesized. Cell transfections were performed using Lipofectamine™ 3000 according to the manufacturer’s instructions. The transfection efficiency was assessed by qRT-PCR and Western blot 48 h post-transfection.
Western blotting
Total cellular proteins were extracted using RIPA lysis buffer (Beyotime, Shanghai, China). Protein concentrations were determined using the BCA Protein Assay Kit. Equal amounts of protein (20 µg) were separated via SDS-PAGE and transferred to PVDF membranes (Beyotime). After blocking with 5% non-fat milk, membranes were incubated with primary antibodies against the ARPC1A (1:1500, #17538-1-AP, Proteintech, Wuhan, China), AURKA (1:1000, #EM1706-71, Huabio, Hangzhou, China), BRD2 (1:1000, #ET7109-07, Huabio), HNRNPL (1:1000, #HA500115, Huabio), METTL3 (1:1000, #HA720002, Huabio), NR4A1 (1:1000, #12235-1-AP, Proteintech), TRPM2 (1:1000, #HA500437, Huabio), and GAPDH (1:5000, #R1210-1, Huabio), followed by HRP-conjugated secondary antibodies (1:10000, #HA1001, Huabio). Protein bands were visualized using ECL reagent and quantified using ImageJ software.
Colony formation assay
Transfected cells were seeded into 6-well plates at a density of 500 cells per well and cultured for 14 days. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Colonies were counted under a light microscope.
Wound healing assay
Cells were seeded in 6-well plates and grown to nearly 90% confluence. A linear scratch was created using a sterile 200-µL pipette tip. Cells were washed with PBS to remove debris and cultured in serum-free medium. Images were captured at 0 and 24 h post-scratch using a microscope.
Transwell invasion assay
Cell invasive ability was assessed using Transwell chambers with 8-µm pore size inserts pre-coated with Matrigel. Briefly, 5 × 10⁴ transfected cells suspended in 200 µL serum-free medium were seeded into the upper chamber. The lower chamber was filled with 600 µL RPMI-1640 medium supplemented with 10% FBS. After incubation at 37°C for 24 h, non-invading cells on the upper surface of the membrane were gently removed. Cells that had invaded through the Matrigel to the lower surface were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted under a light microscope.
Statistical analysis
R software version 4.0.3, SPSS Statistics, and GraphPad Prism version 8.0.2 (GraphPad, CA, USA) were applied for statistical analyses. Student t-test was used to compare the differences between the two groups, and an ANOVA test was applied to compare differences among at least three groups. The survival rate of different MiFeRG expression groups was compared by K-M curves with the log-rank test. The Pearson method was used for correlation analysis. Statistical significance was set as p < 0.05.
Results
Identification and enrichment analysis of differentially expressed MiFeRGs in LUAD
The distribution of disease and normal samples from TCGA-LUAD was shown in Fig. 1A. A total of 9487 DEGs (Table S3) were identified between the normal and tumor samples in the TCGA dataset, including 6604 upregulated genes and 2883 downregulated genes (Fig. 1B). The GeneCard and FerrDb databases obtained 4878 mitophagy-related genes and 380 ferroptosis-related genes, respectively. After the intersection, 136 overlapping differentially expressed MiFeRGs were acquired (Fig. 1C and Table S4).
Fig. 1.
Identification of differentially expressed MiFeRGs in lung cancer. A Distribution of tumor and normal samples in TCGA-LUAD. B Volcano plot of DEGs in lung cancer based on the TCGA dataset. C Overlapping genes of DEGs, mitophagy-related genes, and ferroptosis-related genes. Abbreviations: mitophagy, mitochondrial autophagy; MiFeRGs, mitophagy- and ferroptosis-related genes; DEGs, differentially expressed genes; log2FC, log2 fold change; FRG, ferroptosis-related genes
Moreover, the functions of 136 MiFeRGs were analyzed. In biological progress (BP) terms, these genes were mainly enriched in response to chemical, response to stress, and cellular protein metabolic process (Fig. 2A). Cellular components (CCs) were associated with cytosol, endomembrane system, and protein-containing complex (Fig. 2B), and molecular function (MF) terms were related to catalytic activity, small molecule binding, and enzyme binding (Fig. 2C). KEGG enrichment analysis showed that MiFeRGs were linked to MAPK and PI3K-Akt signaling pathways (Fig. 2D).
Fig. 2.
Functional enrichment analysis of MiFeRGs. A-C GO enrichment analysis for MiFeRGs, including BP A, CC B, and MF C. D KEGG pathway enrichment analysis for MiFeRGs. Abbreviations: MiFeRGs, mitophagy- and ferroptosis-related genes; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes
Establishment and evaluation of a seven-MiFeRG prognostic risk model
To screen genes associated with the survival of patients with LUAD, univariate Cox regression analysis was performed on 136 MiFeRGs. As shown in Figs. 3A, 10 MiFeRGs were identified (p < 0.05). Using LASSO and multivariate Cox regression analysis, 7 genes were finally acquired, including ARPC1A, AURKA, BRD2, HNRNPL, METTL3, NR4A1, and TRPM2 (p < 0.05, Fig. 3B-C). ARPC1A, HNRNPL, and METTL3 were regarded as protective genes [hazard ratio (HR) < 1], while AURKA, BRD2, NR4A1, and TRPM2 served as harmful genes [HR > 1]. Subsequently, we obtained a seven-MiFeRG prognostic model for LUAD patients (risk score = −0.044*ARPC1A + 0.016*AURKA + 0.029*BRD2–0.04*HNRNPL − 0.019*METTL3 + 0.048*NR4A1 + 0.033*TRPM2).
Fig. 3.
Establishment and evaluation of prognostic risk model. A Univariate Cox regression analysis for MiFeRGs. B LASSO Cox regression analysis. C Multivariate Cox regression analysis. D K-M survival curve. E Time-dependent ROC curve. Abbreviations: MiFeRGs, mitophagy- and ferroptosis-related genes; K-M, Kaplan-Meier; ROC, receiver operator characteristic; d, day
To further evaluate the predictive performance of this model, patients were categorized into low-risk or high-risk groups based on the optimal truncation value. Overall survival in the low-risk group was significantly higher than that in the high-risk group (p < 0.0001, Fig. 3D). The time-dependent ROC curve showed that AUC values at 3-, 5-, and 7-year were 0.66, 0.73, and 0.74, respectively (Fig. 3E).
Furthermore, the relevance of risk score with clinical parameters was analyzed, such as age, gender, TNM stages, and pathological stage. No significant difference was observed in these characteristics (p > 0.05, Fig. 4A-F). These results indicated that the risk model had wide applicability and was not affected by these clinical factors.
Fig. 4.
Relevance of risk score with clinical parameters. A-F Risk score at different ages A, genders B, T stages C, N stages D, M stages E, and pathological stages F
Clinical value of seven prognostic MiFeRGs in LUAD
Subsequently, to further explore the association of seven MiFeRGs’ expression with survival rate, survival analysis was performed. Except for NR4A1, the other six genes were all upregulated in the high-risk group (p < 0.01, Fig. 5A). K-M curves revealed that patients with high expression levels of ARPC1A, HNRNPL, METTL3, and NR4A1 had higher survival time, while those with high expression levels of AURKA, BRD2, and TRPM2 had poor prognosis (Fig. 5B-H).
Fig. 5.
Survival analysis of seven prognostic MiFeRGs in lung cancer. A Expression levels of seven prognostic MiFeRGs in low-risk and high-risk groups. B-H K-M curves revealed the survival rate of lung cancer patients in different expression levels of ARPC1A B, AURKA C, BRD2 D, HNRNPL E, METTL3 F, NR4A1 G, and TRPM2 H. Abbreviations: MiFeRGs, mitophagy- and ferroptosis-related genes; K-M, Kaplan-Meier
Moreover, GSEA analysis was used to acquire the pathways related to the seven prognostic MiFeRGs. The enriched pathways included PI3K-Akt-mTOR signaling, mTORC1 signaling, IL6-JAK-STAT3 signaling, inflammatory response, interferon α response, epithelial-mesenchymal transition, and TGF β signaling (Fig. 6A).
Fig. 6.
Prognostic MiFeRG-related pathways. A-G Pathways related to ARPC1A A, AURKA B, BRD2 C, HNRNPL D, METTL3 E, NR4A1 F, and TRPM2 G, respectively. Abbreviation: MiFeRGs, mitophagy- and ferroptosis-related genes
TME analysis
TME plays an important role in multiple interactions between tumor cells and the immune system. To find out the role of TME in different risk groups, we analyzed the KEGG pathways based on the transcriptome data of LUAD individuals in the TCGA database using GSVA. As shown in Fig. 7A, TME-related pathways were enriched, such as mTORC1 and glycolysis, and inflammatory pathway IL6-JAK-STAT3. Considering that immune infiltration in the TME is important for developing novel cancer immunotherapy methods, the differences in immune cells between low-risk and high-risk groups were compared. In comparison with the low-risk group, γ-δ T cells, NK cells, monocytes, M0 macrophages, and mast cells were higher, while CD4 memory T cells and M2 macrophages were lower in the high-risk group (Fig. 7B).
Fig. 7.
GSVA and immune cell infiltration analysis. A GSVA based on KEGG pathways. B Immune cell infiltration in low-risk and high-risk groups. Abbreviations: GSVA, gene set variation analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes
Due to the significant reduction of memory T cells, we speculated that T cell exhaustion might occur due to prolonged inflammation. Therefore, TCR-related gene expression levels were further explored between two risk groups. Figure 8A shows the genes significantly differentially expressed between the low-risk and high-risk groups, such as AKT1, CDC42, and PIK3CA. The correlation of these genes with risk score was visualized in Fig. 8B. CHP1, CHUK, and PIK3CA were the top 3 genes positively correlated with risk scores, while MAP3K14, PIK3R3, and RELA were the top 3 genes that were negatively associated with risk scores (p < 0.001). To further explore the relationship of hub MiFeRGs with TCR genes, we analyzed the interaction of seven prognostic MiFeRGs with TCR-related genes. According to the degree of genes in the PPI network, AURKA was the key prognostic MiFeRG, and AKT1 was the most important TCR-related gene (Fig. 8C).
Fig. 8.
Exploration of T cell exhaustion. A Expression levels of TCR-related genes in low-risk and high-risk groups. B Correlation of TCR-related genes with risk scores. C PPI network of seven prognostic MiFeRGs with TCR-related genes. Abbreviations: TCR, T cell receptor; PPI, protein-protein interaction
Expressional and functional validation of seven hub prognostic MiFeRGs in LUAD cells
To validate the above analytical findings, we further conducted cell-based experiments. First, we compared the expression levels of seven key MiFeRGs between normal human bronchial epithelial cells (BEAS-2B) and three LUAD cell lines (A549, H1975, and NCI-H2009). As shown in Fig. 9A-B, compared to BEAS-2B cells, the expression of ARPC1A, AURKA, BRD2, HNRNPL, METTL3, and TRPM2 was significantly upregulated in A549, H1975, and NCI-H2009 cells, whereas NR4A1 was markedly downregulated (p < 0.01). Among the three LUAD cell lines, the six upregulated genes exhibited the highest expression in A549 cells and the lowest in H1975 cells. Conversely, NR4A1 showed the lowest expression in A549 cells.
Fig. 9.
Expression levels of seven hub prognostic genes in LUAD cells. A Relative mRNA expression levels of ARPC1A, AURKA, BRD2, and HNRNPL. B Relative mRNA expression of METTL3, NR4A1, and TRPM2. **p < 0.01, ***p < 0.001. Abbreviation: LUAD, Lung adenocarcinoma
To further investigate the functional roles of these hub genes in LUAD progression, we manipulated their expression levels for functional validation. In A549 cells, AURKA, BRD2, and TRPM2 were knocked down, while NR4A1 was overexpressed, with satisfactory transfection efficiency (Figure S1A-D). Considering that ARPC1A, HNRNPL, and METTL3 were highly expressed in LUAD but associated with favorable prognosis, we performed bidirectional validation by silencing these genes in A549 cells and overexpressing them in H1975 cells, with efficient transfection confirmed (Figure S2A-F).
Functional assays demonstrated that knockdown of AURKA, BRD2, and TRPM2 or overexpression of NR4A1 significantly inhibited the proliferation, migration, and invasion of A549 cells (p < 0.001, Fig. 10A-F). In contrast, knockdown of ARPC1A, HNRNPL, and METTL3 markedly enhanced the malignant behaviors of A549 cells, whereas overexpression of these genes in H1975 cells effectively suppressed their proliferation, migration, and invasion (p < 0.01, Fig. 11A-F). These findings further support the functional heterogeneity and potential regulatory roles of the identified hub MiFeRGs in LUAD development and progression.
Fig. 10.
Effects of AURKA, BRD2, TRPM2, and NR4A1 on proliferation, migration, and invasion in LUAD cells. A Effects of AURKA, BRD2, and TRPM2 on cell proliferation. B Effects of AURKA, BRD2, and TRPM2 on cell migration; scale bar = 500 μm. C Effects of AURKA, BRD2, and TRPM2 on cell invasion; scale bar = 200 μm. D Effects of NR4A1 on cell proliferation. E Effects of NR4A1 on cell migration; scale bar = 500 μm. F Effects of NR4A1 on cell invasion; scale bar = 200 μm. ***p < 0.001. Abbreviation: LUAD, Lung adenocarcinoma
Fig. 11.
Effects of ARPC1A, HNRNPL, and METTL3 on proliferation, migration, and invasion in LUAD cells. A-B Effect of ARPC1A, HNRNPL, and METTL3 on proliferation in A549 cells A and H1975 cells B. C-D Effect of ARPC1A, HNRNPL, and METTL3 on migration in A549 cells C and H1975 cells D; scale bar = 500 μm. E-F Effect of ARPC1A, HNRNPL, and METTL3 on invasion in A549 cells E and H1975 cells F; scale bar = 200 μm. Abbreviation: LUAD, Lung adenocarcinoma
Discussion
In this study, we identified and analyzed differentially expressed MiFeRGs in LUAD using data from the TCGA database. A prognostic risk model was established based on seven selected MiFeRGs (ARPC1A, AURKA, BRD2, HNRNPL, METTL3, NR4A1, and TRPM2) and evaluated its predictive performance. Additionally, we explored the prognostic significance of these genes and their association with immune cell infiltration and T-cell exhaustion to provide insights into potential therapeutic targets and personalized treatment strategies for LUAD patients.
Cancer cells require more iron than normal cells, and imbalances in iron metabolism can increase the risk of cancer and promote tumor growth [19]. Ferroptosis is highly dependent on the production of ROS, with mitochondria being the main source of ROS within cells, closely related to ferroptosis. The generation of mitochondrial ROS may promote ferroptosis by promoting lipid peroxidation [20]. Mitochondrial regulatory molecules, such as mitophagy-related genes, are involved in ferroptosis. PINK1-related mitophagy regulates the process of cellular ferroptosis [17]. The ferroptosis pathway induced by mitophagy has been studied in some cancers, such as pancreatic cancer [21] and melanoma [22]. Targeting ferroptosis or mitophagy has been proven to be a potential strategy for treating lung cancer [23, 24]. Liu et al. found that the ferroptosis inducer erastin combined with celastrol significantly increased the expression of mitochondrial autophagy regulatory pathway proteins PINK1 and Parkin in NSCLC cells in an HSF1-dependent manner [25]. Another study found that COX7A1 can increase the sensitivity of NSCLC cells to ferroptosis and inhibit mitochondrial autophagy in lung cancer cells by blocking autophagic flux [26]. In this study, we identified 136 MiFeRGs, and based on these genes, we finally obtained a seven-MiFeRGs prognostic risk model through univariate, LASSO, and multivariate Cox regression analyses. This model demonstrated good efficacy in predicting the survival rate of LUAD patients. Furthermore, the application of the model is not significantly affected by clinical factors such as age, gender, TNM staging, and pathological staging.
The seven hub prognostic genes identified in our study (AURKA, BRD2, TRPM2, NR4A1, ARPC1A, HNRNPL, and METTL3) are closely associated with the occurrence and progression of LUAD. Increased AURKA expression has been linked to poor prognosis in various cancers [27] and has been shown to negatively regulate ferroptosis [28]. AURKA significantly influences lung cancer cell proliferation and migration [29]. BRD2 is a genetic susceptibility gene for lung cancer and is associated with an increased risk of lung cancer [30]. Inhibition of the BRD2-FTH1 axis has been shown to induce ferroptosis in NSCLC [31]. TRPM2 is an important regulator of Ca²⁺ influx, and silencing TRPM2 induces G2/M arrest and apoptosis in lung cancer cells by increasing intracellular ROS levels [32, 33]. Consistent with these studies, our findings revealed that AURKA, BRD2, and TRPM2 are upregulated in LUAD and are associated with poor prognosis, while their knockdown significantly inhibited LUAD cell proliferation, migration, and invasion. NR4A1 (Nur77) is an orphan nuclear receptor that can induce apoptosis and mediate chemotherapy-induced cancer cell death [34]. Although some studies have reported that NR4A1 is upregulated in lung cancer and associated with poor prognosis [35], its function appears to be cell type- and drug context-dependent [36]. Hu et al. demonstrated that malate-induced Nur77 expression and mitochondrial translocation effectively suppressed tumor cell growth in NSCLC [37]. Our study revealed that NR4A1 is downregulated in LUAD cells, and its overexpression significantly inhibited LUAD cell proliferation, migration, and invasion. ARPC1A, a member of the ARP2/3 complex family, is involved in cytoskeleton remodeling, an essential process for tumor metastasis. ARPC1A has been reported to be upregulated in lung cancer [38], and its overexpression promotes lung metastasis in prostate cancer in vivo [39]. HNRNPL, an RNA-binding protein primarily located in the nucleus, regulates cell proliferation and apoptosis in various cancers [40, 41]. METTL3 reduces the sensitivity of lung cancer cells to ferroptosis by regulating TFRC [42] and inhibits lung cancer invasion through SH3BP5 methylation modification [43]. In our study, ARPC1A, HNRNPL, and METTL3 were highly expressed in LUAD samples; however, their high expression was associated with better prognosis. To further explore this discrepancy, we performed bidirectional regulation of their expression. Knockdown of ARPC1A, HNRNPL, and METTL3 promoted LUAD cell proliferation, migration, and invasion, whereas their overexpression suppressed these malignant phenotypes. These findings suggest that ARPC1A, HNRNPL, and METTL3 may possess the potential to inhibit malignant phenotypes in LUAD, and their high expression may represent a stress response or compensatory protective mechanism during tumor progression.
GSVA analysis further explored the potential pathways associated with these hub genes and found significant enrichment of the immune-inflammatory pathway IL6-JAK-STAT3 signaling. The JAK-STAT3 pathway mediates anti-apoptotic and angiogenesis functions in the TME and is associated with immunosuppressive functions [44]. IL6 activates the JAK-STAT3 pathway by binding to its specific receptor IL6R [45]. Several hub MiFeRGs identified in this study may regulate the IL6–JAK–STAT3 signaling pathway. For instance, AURKA overexpression competitively interferes with STAT3 nuclear translocation, thereby modulating JAK2–STAT3 activity in gastric and esophageal cancers [46]. HNRNPL has been implicated in the regulation of IL6–JAK–STAT3 signaling in cancer [47]. METTL3 mediates m6A modification of circSTX6, leading to activation of the IL6–JAK–STAT3 pathway in cervical cancer [48]. Additionally, TRPM2 is involved in the IL-6 pathway, and its deficiency results in reduced IL-6 production [49]. Dysregulation of the IL6-JAK-STAT3 axis is a major factor in the progression of NSCLC [50]. Considering the significant impact of the immune microenvironment on LUAD development, we performed an immune infiltration analysis. In the high-risk group, anti-inflammatory M2 macrophages were significantly decreased, while mast cells secreting inflammatory factors were significantly increased. Additionally, NK cells were highly expressed in the high-risk group. We speculate that NK cells may not play a key role in resisting tumor deterioration but may increase to inhibit inflammatory reactions. Based on the significant decrease in memory T cells, it is inferred that T cell exhaustion may occur due to long-term inflammation.
T cell exhaustion is an immune dysregulation phenomenon observed in cancer and is considered a resistant pathway to cellular immunotherapy [51]. Mary Philip and Andrea Schietinger defined T cell exhaustion as a differentiation state observed during chronic infection under sustained antigen and chronic TCR stimulation [52]. Therefore, we further studied TCR-related genes. We identified TCR-related genes that were differentially expressed in the high and low-risk groups. Through STRING analysis, we constructed a PPI network of TCR-related genes with the 7 hub prognostic genes. According to the degree value, it was found that AURKA and the TCR-related gene AKT1 had higher importance. Previous studies have shown that AKT1 inhibition prevents the development of lung tumors in mice [53]. Upregulation of AURKA can activate AKT1 to maintain the malignant state of acute myeloid leukemia cells [54]. In gastric cancer, AURKA induces EMT through the AKT signaling pathway [55]. We observed obvious upregulation of AURKA mRNA expression and protein expression in LUAD cells and tissues. Interestingly, in this study, GSEA analysis found that AURKA was associated with activation of the mTORC1 signal. The mTORC1 signal is associated with EMT in cancer, and inhibition of mTORC1 in LUAD cells can induce activation of the Akt signaling pathway [56]. Based on our research results, we speculate that AURKA may influence the invasion and metastasis of LUAD through the mTORC1/AKT pathway. This speculation needs further experimental validation.
There are several limitations in this study. First, although we performed comprehensive bioinformatics analyses and partial experimental validation, the specific molecular mechanisms require further in vitro and in vivo investigations. Second, while the prognostic risk model demonstrated robust predictive performance across multiple clinical subgroups, its generalizability and applicability need to be validated in larger independent cohorts to confirm its clinical utility.
Conclusion
In summary, this analysis elucidates the significance of MiFeRGs in LUAD progression and prognosis. The developed prognostic risk model, based on 7 MiFeRGs, demonstrates promise predictive performance. Furthermore, insights into immune infiltration, TCR-related genes, and pathway analysis shed light on the intricate interplay between tumor microenvironment and therapeutic responses. These findings contribute to our understanding of LUAD pathogenesis and offer potential therapeutic targets for precision medicine approaches.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- Mitophagy
Mitochondrial autophagy
- MiFeRGs
Mitophagy- and ferroptosis-related genes
- DEGs
Differentially expressed genes
- log2FC
log2 fold change
- FRG
Ferroptosis-related genes
Authors’ contributions
Conceptualization: YNZ, JYW.Data curation: YNZ, BFH.Formal analysis: YNZ, TWL, DL.Investigation: JYW, HW.Methodology: JYW, HYL, YL.Writing – original draft: YNZ, JYW, HYL, DL, TWL, BFH, KF, HW, YL.Writing – review & editing: BFH, JYW.
Funding
This study was supported by grants from the Langfang Science and Technology Research and Development Program Projects (No. 2024013103).
Data availability
The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The Ethics Committee of Hebei CNPC Central Hospital deemed that this research is based on open-source data, so the need for ethics approval was waived.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Ji-yun Wang, Email: Wangjiyun@163.com.
Bo-feng Hou, Email: 15097391017@163.com.
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Associated Data
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Supplementary Materials
Data Availability Statement
The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.